guide: begin How To section

content/docs/sidebar.json

@@ -98,7 +98,12 @@
           "katacoda": "https://katacoda.com/dvc/courses/examples/dvcignore"
         }
       },
-      "updating-tracked-files",
+      {
+        "label": "How To",
+        "slug": "how-to",
+        "source": false,
+        "children": ["best-practices", "update-tracked-files"]
+      },
       "setup-google-drive-remote",
       "large-dataset-optimization",
       "external-dependencies",

content/docs/user-guide/how-to/best-practices.md

@@ -0,0 +1,74 @@
+# Best Practices for DVC Projects
+
+Data scientists, engineers, or managers may already know or can easily find
+answers to some of these questions. However, the variety of answers and
+approaches makes data science collaboration a nightmare. **A systematic approach
+is required.**
+
+## Questions on...
+
+### Source code and data versioning
+
+- How do you avoid discrepancies between
+  [revisions](https://git-scm.com/docs/revisions) of source code and versions of
+  data files, when the data cannot fit into a traditional repository?
+
+  DVC replaces all large data files, models, etc. with small
+  [metafiles](doc/user-guide/dvc-files-and-directories) (tracked by Git). These
+  files point to the original data, which you can access by checking out the
+  required `revision`.
+
+### Experiments
+
+- How do you document progress of training different types of models on your
+  data files in the same project?
+
+  You can make use of Git branches for each of the model and then utilise DVC
+  features while working on that branch.
+
+### Experiment time log
+
+- How do you track which of your
+  [hyperparameter](<https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>)
+  changes contributed the most to producing or improving your target
+  [metric](doc/command-reference/metrics)? How do you monitor the degree of each
+  change?
+
+  Hyperparameters are defined using the the `--params` option of `dvc run` and
+  the default parameters file is `params.yaml`. You can commit different
+  versions of `params.yaml` and then use `dvc metrics` or `dvc plots` to track
+  which parameter contributes most to the change.
+
+### Navigating through experiments
+
+- How do you recover a model from last week without wasting time waiting for the
+  model to retrain?
+
+  First you can checkout the required `revision`, followed by `dvc checkout` to
+  update DVC-tracked files and directories in your workspace.
+
+- How do you quickly switch between a large dataset and a small subset without
+  modifying source code?
+
+  You can change dependencies of relevant stage either by using `dvc run` with
+  `-f` option or by manually editing the stage in `dvc.yaml` file.
+
+### Reproducibility
+
+- How do you run a model's evaluation process again without retraining the model
+  and preprocessing a raw dataset?
+
+  DVC provides a way to reproduce pipelines partially. You can use `dvc repro`
+  to execute evaluation stage without reproducing complete pipeline.
+
+### Managing and sharing large data files
+
+- How do you share models trained in a GPU environment with colleagues who don't
+  have access to a GPU?
+
+- How do you share the entire 147 GB of your ML project, with all of its data
+  sources, intermediate data files, and models?
+
+  Cloud or local storage can be used to store the project's data. You can share
+  large data files and models with others if they are stored on
+  [remote storage](doc/command-reference/remote/add#supported-storage-types).